def con(i, now):
if len(now) == len(patt):
yield now
elif i < len(perm):
nxt = now + [perm[i]]
if Permutation.to_standard(nxt) == Permutation.to_standard(patt[:len(nxt)]):
for res in con(i+1, nxt):
yield res
for res in con(i+1, now):
yield res
def _assure_length(self, l):
if l in self.permutations:
return
if l == 0:
self.permutations[0] = [Permutation()]
else:
self._assure_length(l - 1)
here = set()
for p in self.permutations[l - 1]:
for i in range(l):
q = p[:i] + (l, ) + p[i:]
if Permutation(q).avoids(*self.avoid):
here.add(q)
self.permutations[l] = list(here)
def sub_mesh_pattern(self, indices):
"""Return the mesh pattern induced by indices.
Args:
self:
A mesh pattern.
indices: <collections.Iterable> of <numbers.Integral>
A list of unique indices of elements in self.
Returns: permuta.MeshPattern
A mesh pattern where the pattern is the permutation induced by the
indices and a region is shaded if and only if the corresponding
region of self is fully shaded.
"""
indices = sorted(indices)
if not indices:
return MeshPattern()
pattern = Permutation.to_standard(self.pattern[index] for index in indices)
vertical = [0]
vertical.extend(index + 1 for index in indices)
vertical.append(len(self) + 1)
horizontal = [0]
horizontal.extend(sorted(self.pattern[index] for index in indices))
horizontal.append(len(self) + 1)
shading = frozenset((x, y)
for x in range(len(pattern) + 1)
for y in range(len(pattern) + 1)
if self.is_shaded((vertical[x],
horizontal[y]),
(vertical[x+1] - 1,
horizontal[y+1] - 1)))
return MeshPattern(pattern, shading)
def test_direct_sum(self):
p1 = Permutation(1243)
p2 = Permutation(15324)
p3 = Permutation(312)
p4 = Permutation(1)
p5 = Permutation()
# All together
result = p1.direct_sum(p2, p3, p4, p5)
expected = Permutation((1,2,4,3,5,9,7,6,8,12,10,11,13))
self.assertEqual(result, expected)
# Two
result = p1.direct_sum(p3)
expected = Permutation((1,2,4,3,7,5,6))
self.assertEqual(result, expected)
# None
self.assertEqual(p1.direct_sum(), p1)
def test_skew_sum(self):
p1 = Permutation(1243)
p2 = Permutation(15324)
p3 = Permutation(312)
p4 = Permutation(1)
p5 = Permutation()
# All together
result = p1.skew_sum(p2, p3, p4, p5)
expected = Permutation((10,11,13,12,5,9,7,6,8,4,2,3,1))
self.assertEqual(result, expected)
# Two
result = p1.skew_sum(p3)
expected = Permutation((4,5,7,6,3,1,2))
self.assertEqual(result, expected)
# None
self.assertEqual(p1.skew_sum(), p1)
def occurrences_in(self, perm):
"""Find all indices of occurrences of self in perm.
Args:
self:
The mesh pattern whose occurrences are to be found.
perm: permuta.Permutation
The permutation to search for occurrences in.
Yields: [int]
The indices of the occurrences of self in perm. Each yielded element
l is a list of integer indices of the permutation perm such that
self.pattern == permuta.Permutation.to_standard([perm[i] for i in l])
and that no element not in the occurrence falls within self.shading.
"""
# TODO: Implement all nice
indices = list(range(len(perm)))
for candidate_indices in itertools.combinations(indices, len(self)):
candidate = [perm[index] for index in candidate_indices]
if Permutation.to_standard(candidate) != self.pattern:
continue
x = 0
for element in perm:
if element in candidate:
x += 1
continue
y = sum(1 for candidate_element in candidate
if candidate_element < element)
if (x, y) in self.shading:
break
else:
# No unused point fell within shading
yield list(candidate_indices)
def add_rule(self, rule):
self.total_rules += 1
bs = 0
curcnt = 0
empty = True
for l in range(self.settings.perm_bound+1):
curlevel = []
for perm in rule.generate_of_length(l, self.settings.sinput.permutations):
if not self.settings.sinput.contains(perm):
# the rule generated something that doesn't satisfy perm_prop
self.death_by_perm_prop += 1
return RuleDeath.PERM_PROP
curlevel.append(perm)
cur = sorted(curlevel)
for a,b in zip(cur, cur[1:]):
if a == b:
# the rule generated something more than once (i.e. there is overlap)
self.death_by_overlap += 1
return RuleDeath.OVERLAP
i = 0
j = 0
while i < len(cur) and j < len(self.permset[l]):
if self.permset[l][j] < Permutation(cur[i]):
j += 1
curcnt += 1
elif Permutation(cur[i]) == self.permset[l][j]:
bs |= 1 << curcnt
i += 1
j += 1
curcnt += 1
empty = False
else:
assert False
assert i == len(cur)
curcnt += len(self.permset[l]) - j
if empty:
assert False
self.rules.setdefault(bs, [])
self.rules[bs].append(rule)
return RuleDeath.ALIVE
def _assure_length(self, l):
if l in self.permutations:
return
if l == 0:
self.permutations[0] = []
maybe = Permutation([])
if maybe.avoids(*self.avoidance):
self.permutations[0].append(maybe)
else:
self._assure_length(l-1)
here = set()
for p in self.permutations[l-1]:
for i in range(l):
q = Permutation(p[:i] + (l,) + p[i:])
if q.avoids(*self.avoidance):
here.add(q)
self.permutations[l] = list(here)
def test_to_standard(self):
def gen(perm):
res = list(perm)
add = 0
for i in perm.inverse():
add += random.randint(0,10)
res[i-1] += add
return Permutation(res)
for i in range(100):
perm = Permutations(random.randint(0,20)).random_element()
self.assertEqual(perm, Permutation.to_standard(perm))
self.assertEqual(perm, Permutation.to_standard(gen(perm)))
self.assertEqual(Permutation.to_standard(range(10)),
Permutation((1,2,3,4,5,6,7,8,9,10)))
self.assertEqual(Permutation.to_standard(10 - x for x in range(5)),
Permutation((5,4,3,2,1)))
def _assure_length(self, l):
if l in self.permutations:
return
if l == 0:
self.permutations[0] = []
maybe = Permutation([])
if maybe.avoids(*self.avoidance):
self.permutations[0].append(maybe)
else:
self._assure_length(l - 1)
here = set()
for p in self.permutations[l - 1]:
for i in range(l):
q = Permutation(p[:i] + (l, ) + p[i:])
if q.avoids(*self.avoidance):
here.add(q)
self.permutations[l] = here
def taylor_dag(settings, max_len_patt=None, upper_bound=None, remove=False, remove_finite=True,
subpattern_type=SubPatternType.EVERY):
assert settings.sinput.avoidance is not None, "Tayloring is only supported for avoidance"
patterns = settings.sinput.avoidance
started = datetime.datetime.now()
settings.logger.log('Tayloring DAG')
elems = []
if len(patterns) > 0:
if max_len_patt is None:
max_len_patt = max( len(p) for p in patterns )
n = max( len(p) for p in patterns )
sub = [ set([]) for _ in range(len(patterns)) ]
for i,p in enumerate(patterns):
last = set([ p ])
if len(p) <= max_len_patt:
sub[i] |= last
for l in range(len(p)-1, 1, -1):
nxt = set([])
for q in last:
for j in range(len(q)):
qp = Permutation([ x-1 if x>q[j] else x for x in q[:j] + q[j+1:] ])
nxt.add(qp)
if l <= max_len_patt:
sub[i] |= nxt
last = nxt
valid = set()
for i,p in enumerate(patterns):
if subpattern_type == SubPatternType.RECTANGULAR:
for l in range(len(p)):
for r in range(l,len(p)):
here = sorted(p.perm[l:r+1])
for x in range(len(here)):
want = set()
for y in range(x,len(here)):
want.add(here[y])
cur = []
for j in range(l,r+1):
if p.perm[j] in want:
cur.append(p.perm[j])
if len(cur) <= max_len_patt:
valid.add(Permutation.to_standard(cur))
elif subpattern_type == SubPatternType.CONSECUTIVE:
for l in range(len(p)):
for r in range(l,len(p)):
if r - l + 1 > max_len_patt:
break
here = p.perm[l:r+1]
if max(here) - min(here) + 1 == len(here):
valid.add(Permutation.to_standard(here))
if subpattern_type != SubPatternType.EVERY:
for i,p in enumerate(patterns):
sub[i] &= valid
def can_add(add, picked):
for p in picked:
if len(p) > len(add) and p.contains(add):
return False
if len(add) > len(p) and add.contains(p):
return False
return True
def bt(at,picked,seen):
if upper_bound is not None and len(picked) > upper_bound:
pass
elif picked == set(patterns):
pass
elif at == len(patterns):
yield picked
else:
for s in subsets(list(sub[at] - picked - seen)):
npicked = set(picked)
ok = True
for add in set(s):
if not can_add(add, npicked):
ok = False
break
npicked.add(add)
if not ok:
continue
found = False
for q in npicked:
if patterns[at].contains(q):
found = True
break
if not found:
continue
for res in bt(at+1,npicked,seen | sub[at]):
yield res
for ps in bt(0,set(),set()):
# if remove_av_incr_decr:
# if any( p.is_increasing() for p in ps ) and any( p.is_decreasing() for p in ps ):
# continue
s = AvoiderPermutationSet(ps)
s._assure_length(settings.perm_bound)
here = set()
finite = False
for l in range(settings.perm_bound+1):
found = False
for p in s.generate_of_length(l, {}):
here.add(Permutation(list(p)))
found = True
if not found:
finite = True
if finite and remove_finite and settings.sinput.is_classical:
break
if finite and remove_finite and settings.sinput.is_classical:
continue
here = { Permutation(list(p)) for l in range(settings.perm_bound+1) for p in s.generate_of_length(l, {}) }
elems.append((s, here, None))
input = settings.sinput.get_permutation_set()
elems.append((InputPermutationSet(settings), input, None))
elems.append((N, set([ Permutation([]) ]), None))
elems.append((P, set([ Permutation([1]) ]), None))
res = DAG()
if remove:
for ps,here,descr in elems:
rem = set()
rems = []
for qs,other,odescr in elems:
if other and other < here:
rems.append(qs)
rem |= other
if not here-rem:
continue
if rems:
res.add_element(SubtractPermutationSet(ps, rems, alone_ok=type(ps) is not InputPermutationSet))
else:
res.add_element(ps)
else:
for ps,here,descr in elems:
res.add_element(ps)
for ps,here,descr in elems:
for qs,other,odescr in elems:
if other and other < here:
res.put_below(qs,ps)
ended = datetime.datetime.now()
settings.logger.log('Finished in %.3fs' % (ended - started).total_seconds())
return res
def decr(n):
return SimpleGeneratingRule(Permutation([2, 1]), [X, P],
description='decreasing').to_static(n, empty)
from permuta.misc import ordered_set_partitions, flatten
from permstruct.permutation_sets import PermutationSet, StaticPermutationSet, PointPermutationSet, InputPermutationSet
from permstruct import X, N, P, empty, generate_all_of_length
from permuta import Permutation, Permutations
from permstruct.permutation_sets import SimpleGeneratingRule, OverlayGeneratingRule
from itertools import product
from copy import deepcopy
incr = SimpleGeneratingRule(Permutation([1, 2]), [X, P],
description='increasing').to_static(8, empty)
decr = SimpleGeneratingRule(Permutation([2, 1]), [X, P],
description='decreasing').to_static(8, empty)
def at_most_one_descent(p):
found = False
for i in range(1, len(p)):
if p[i - 1] > p[i]:
if found:
return False
found = True
return True
av123 = set([
tuple(p) for l in range(8) for p in Permutations(l) if p.avoids([1, 2, 3])
])
# print(sorted(av123))
def construct_rule(perm_prop,
perm_bound,
dag,
max_rule_size,
max_nonempty,
max_rules,
ignore_first=1,
allow_overlap_in_first=True):
"""Tries to construct a set of generating rules that together generate the
given permutation set.
INPUT:
- ``perm_prop`` - the permutation set expressed as a boolean predicate:
perm_prop(p) should return True iff the permutation p is a part of the
permutation set.
- ``perm_bound`` - consider only permutations of length up to perm_bound, inclusive.
- ``max_rule_size`` - a tuple (n, m) specifying that we should consider
generating rules with at most n rows and at most m columns.
- ``max_nonempty`` - the maximum number of non-empty boxes in the resulting
generating rule.
- ``max_rules`` - the maximum number of generating rules to match
together.
- ``dag`` - a list of tuples (f, g), where g is a generating rule and f
is a boolean predicate: f(p) should return True iff the permutation p can
be produced by g. These generating rules are possible candidates for
boxes in the resulting generating rules.
- ``ignore_first`` - ignore the smallest ``ignore_first`` permutations when
doing the generating rule matching. So even if a set of generating rules
don't produce a certain small permutation, they will still be considered.
- ``allow_overlap_in_first`` - whether to allow overlap (that is, the same
permutation being produced multiple times) in the ``ignore_first``
permutations.
Note that if performance is an issue, the parameters ``perm_bound``, ``max_rule_size``,
``max_nonempty``, ``max_rules`` and ``dag`` can be tweaked
for better performance, at the cost of lower number of results.
This function currently doesn't return anything meaningful, but it does
display its results to stdout.
"""
main_perms = []
for perm in Permutations(perm_bound):
if perm_prop(perm):
main_perms.append(tuple(perm))
# pick the main permutation to work with, currently just chooses one of the
# largest ones randomly
# TODO: be more smart about picking the permutations to learn from (or use all of them)
random.shuffle(main_perms)
main_perms = main_perms[:50]
# main_perm = [ Permutation([1,2,3,4,5,6]) ]
rules = RuleSet(perm_prop, perm_bound)
tried_rules = set()
for n in range(1, max_rule_size[0] + 1):
for m in range(1, max_rule_size[1] + 1):
for xsep in choose(perm_bound - 1, n - 1):
for ysep in choose(perm_bound - 1, m - 1):
for main_perm in main_perms:
arr = [[[] for j in range(m)] for i in range(n)]
nonempty_cnt = 0
ok = True
for i in range(n):
for j in range(m):
for k in range(
0 if j == 0 else ysep[j - 1] + 1,
(perm_bound - 1 if j == m - 1 else ysep[j])
+ 1):
if (0 if i == 0 else xsep[i - 1] + 1
) <= perm_bound - main_perm[k] <= (
perm_bound - 1 if i == n -
1 else xsep[i]):
arr[i][j].append(main_perm[k])
if arr[i][j]:
nonempty_cnt += 1
if nonempty_cnt > max_nonempty:
ok = False
break
if not ok:
break
if not ok:
continue
nonempty = []
for i in range(n):
for j in range(m):
if arr[i][j]:
arr[i][j] = Permutation.to_standard(
arr[i][j])
cur = []
# for inp_prop, inp in dag.elements:
for inp in dag.elements:
if inp is None:
continue
if inp.contains(arr[i][j]):
cur.append((i, j, inp))
nonempty.append(cur)
for poss in product(*nonempty):
rule = GeneratingRule({(i, j): inp
for i, j, inp in poss})
if rule in tried_rules:
continue
# print(rule)
tried_rules.add(rule)
rules.add_rule(rule)
print('Found %d rules, %d of which are valid, %d of which are distinct' % (
len(tried_rules),
sum(len(v) for k, v in rules.rules.items()),
len(rules.rules),
))
return rules.exact_cover(
max_rules,
ignore_first,
allow_overlap_in_first,
)
from permuta import Permutation
from permstruct import *
from permstruct.dag import *
patts = [ Permutation([1,3,2]) ]
# patts = [ Permutation([1,3,2]), Permutation([4,3,1,2]) ]
# patts = [ Permutation([]), Permutation([]) ]
# patts = [Permutation([1,4,3,2]), Permutation([4,2,3,1])]
# patts = [Permutation([1,3,2,4])]
# patts = [Permutation([1, 4, 2, 3]), Permutation([2, 1, 4, 3]), Permutation([2, 4, 3, 1]), Permutation([3, 1, 4, 2]), Permutation([3, 2, 1, 4]), Permutation([3, 2, 4, 1]), Permutation([3, 4, 1, 2]), Permutation([4, 1, 2, 3]), Permutation([4, 2, 1, 3]), Permutation([4, 2, 3, 1]), Permutation([4, 3, 2, 1])]
# patts = [Permutation([3,2,1]), Permutation([2,1,4,3])]
# patts = [ Permutation([1,2,3]) ]
# patts = [ Permutation([1,3,2,4]) ]
perm_bound = 7
settings = StructSettings(
perm_bound=perm_bound,
verify_bound=7,
max_rule_size=(3,3),
max_non_empty=3,
verbosity=StructLogger.INFO)
# settings.set_input(StructInput.from_avoidance(settings, patts))
settings.set_input(AvoiderInput(settings, patts))
# settings.set_dag(taylor_dag(settings, max_len_patt=3, remove=True, upper_bound=3))
# settings.set_dag(taylor_dag(settings, remove=False))
# el = taylor_dag(settings, remove=True, max_len_patt=2, upper_bound=1).elements
# print(el)
# print(len(el))
dag = taylor_dag(settings, remove=False, subpattern_type=SubPatternType.EVERY)
def construct_rule(perm_prop,
perm_bound,
dag,
max_rule_size,
max_nonempty,
max_rules,
ignore_first=1,
allow_overlap_in_first=True):
"""Tries to construct a set of generating rules that together generate the
given permutation set.
INPUT:
- ``perm_prop`` - the permutation set expressed as a boolean predicate:
perm_prop(p) should return True iff the permutation p is a part of the
permutation set.
- ``perm_bound`` - consider only permutations of length up to perm_bound, inclusive.
- ``max_rule_size`` - a tuple (n, m) specifying that we should consider
generating rules with at most n rows and at most m columns.
- ``max_nonempty`` - the maximum number of non-empty boxes in the resulting
generating rule.
- ``max_rules`` - the maximum number of generating rules to match
together.
- ``dag`` - a list of tuples (f, g), where g is a generating rule and f
is a boolean predicate: f(p) should return True iff the permutation p can
be produced by g. These generating rules are possible candidates for
boxes in the resulting generating rules.
- ``ignore_first`` - ignore the smallest ``ignore_first`` permutations when
doing the generating rule matching. So even if a set of generating rules
don't produce a certain small permutation, they will still be considered.
- ``allow_overlap_in_first`` - whether to allow overlap (that is, the same
permutation being produced multiple times) in the ``ignore_first``
permutations.
Note that if performance is an issue, the parameters ``perm_bound``, ``max_rule_size``,
``max_nonempty``, ``max_rules`` and ``dag`` can be tweaked
for better performance, at the cost of lower number of results.
This function currently doesn't return anything meaningful, but it does
display its results to stdout.
"""
main_perms = []
for perm in Permutations(perm_bound):
if perm_prop(perm):
main_perms.append(tuple(perm))
# pick the main permutation to work with, currently just chooses one of the
# largest ones randomly
# TODO: be more smart about picking the permutations to learn from (or use all of them)
random.shuffle(main_perms)
main_perms = main_perms[:50]
# main_perm = [ Permutation([1,2,3,4,5,6]) ]
rules = RuleSet(perm_prop, perm_bound)
tried_rules = set()
for n in range(1, max_rule_size[0] + 1):
for m in range(1, max_rule_size[1] + 1):
for xsep in choose(perm_bound - 1, n - 1):
for ysep in choose(perm_bound - 1, m - 1):
for main_perm in main_perms:
arr = [ [ [] for j in range(m) ] for i in range(n) ]
nonempty_cnt = 0
ok = True
for i in range(n):
for j in range(m):
for k in range(0 if j == 0 else ysep[j-1] + 1, (perm_bound - 1 if j == m - 1 else ysep[j]) + 1):
if (0 if i == 0 else xsep[i-1] + 1) <= perm_bound - main_perm[k] <= (perm_bound - 1 if i == n - 1 else xsep[i]):
arr[i][j].append(main_perm[k])
if arr[i][j]:
nonempty_cnt += 1
if nonempty_cnt > max_nonempty:
ok = False
break
if not ok:
break
if not ok:
continue
nonempty = []
for i in range(n):
for j in range(m):
if arr[i][j]:
arr[i][j] = Permutation.to_standard(arr[i][j])
cur = []
# for inp_prop, inp in dag.elements:
for inp in dag.elements:
if inp is None:
continue
if inp.contains(arr[i][j]):
cur.append((i, j, inp))
nonempty.append(cur)
for poss in product(*nonempty):
rule = GeneratingRule({ (i,j): inp for i, j, inp in poss })
if rule in tried_rules:
continue
# print(rule)
tried_rules.add(rule)
rules.add_rule(rule)
print('Found %d rules, %d of which are valid, %d of which are distinct' % (
len(tried_rules),
sum( len(v) for k, v in rules.rules.items() ),
len(rules.rules),
))
return rules.exact_cover(
max_rules,
ignore_first,
allow_overlap_in_first,
)
return not is_polynomial(C)
def wrong_is_polynomial(C):
overallinterset = set([])
for perm in C:
overallinterset = overallinterset.union(wrong_types(perm))
if len(overallinterset) == 10:
return True
return False
def wrong_is_non_polynomial(C):
return not wrong_is_polynomial(C)
while True:
line = sys.stdin.readline()
if not line:
break
line = line.strip()
perms = [Permutation(list(map(int, p))) for p in line.split('_')]
if is_non_polynomial(perms):
print line
# Use the stuff below to see what we first missed
# winp = wrong_is_non_polynomial(perms)
# inp = is_non_polynomial(perms)
# if winp != inp:
# print line
def generate_of_length(self, n, input):
rule = list(self.rule.items())
h = max( k[0] for k,v in rule ) + 1 if rule else 1
w = max( k[1] for k,v in rule ) + 1 if rule else 1
def permute(arr, perm):
res = [None] * len(arr)
for i in range(len(arr)):
res[i] = arr[perm[i] - 1]
return res
def count_assignments(at, left):
if at == len(rule):
if left == 0:
yield []
elif type(rule[at][1]) is PointPermutationSet:
# this doesn't need to be handled separately,
# it's just an optimization
if left > 0:
for ass in count_assignments(at + 1, left - 1):
yield [1] + ass
else:
for cur in range(left+1):
for ass in count_assignments(at + 1, left - cur):
yield [cur] + ass
for count_ass in count_assignments(0, n):
for perm_ass in product(*[ s[1].generate_of_length(cnt, input) for cnt, s in zip(count_ass, rule) ]):
arr = [ [ [] for j in range(w) ] for i in range(h) ]
for i, perm in enumerate(perm_ass):
arr[rule[i][0][0]][rule[i][0][1]] = perm
rowcnt = [ sum( len(arr[row][col]) for col in range(w) ) for row in range(h) ]
colcnt = [ sum( len(arr[row][col]) for row in range(h) ) for col in range(w) ]
for colpart in product(*[ ordered_set_partitions(range(colcnt[col]), [ len(arr[row][col]) for row in range(h) ]) for col in range(w) ]):
for rowpart in product(*[ ordered_set_partitions(range(rowcnt[row]), [ len(arr[row][col]) for col in range(w) ]) for row in range(h) ]):
ok = True
for idxs, perm_set in self.overlay:
res = [ [None]*colcnt[col] for col in range(w) ]
cumul = 1
for row in range(h-1,-1,-1):
for col in range(w):
if (row,col) in idxs:
for idx, val in zip(sorted(colpart[col][row]), permute(sorted(rowpart[row][col]), arr[row][col])):
res[col][idx] = cumul + val
cumul += rowcnt[row]
perm = Permutation.to_standard(flatten(res))
if not perm_set.contains(perm):
ok = False
break
if not ok:
continue
res = [ [None]*colcnt[col] for col in range(w) ]
cumul = 1
for row in range(h-1,-1,-1):
for col in range(w):
for idx, val in zip(sorted(colpart[col][row]), permute(sorted(rowpart[row][col]), arr[row][col])):
res[col][idx] = cumul + val
cumul += rowcnt[row]
yield Permutation(flatten(res))
def decr_nonempty(n):
return SimpleGeneratingRule(Permutation([2, 1]), [X, P],
description='decreasing nonempty').to_static(
n, {1: [Permutation([1])]})
def taylor_dag(settings,
max_len_patt=None,
upper_bound=None,
remove=False,
remove_finite=True,
subpattern_type=SubPatternType.EVERY):
assert settings.sinput.avoidance is not None, "Tayloring is only supported for avoidance"
patterns = settings.sinput.avoidance
started = datetime.datetime.now()
settings.logger.log('Tayloring DAG')
elems = []
if len(patterns) > 0:
if max_len_patt is None:
max_len_patt = max(len(p) for p in patterns)
n = max(len(p) for p in patterns)
sub = [set([]) for _ in range(len(patterns))]
for i, p in enumerate(patterns):
last = set([p])
if len(p) <= max_len_patt:
sub[i] |= last
for l in range(len(p) - 1, 1, -1):
nxt = set([])
for q in last:
for j in range(len(q)):
qp = Permutation([
x - 1 if x > q[j] else x for x in q[:j] + q[j + 1:]
])
nxt.add(qp)
if l <= max_len_patt:
sub[i] |= nxt
last = nxt
valid = set()
for i, p in enumerate(patterns):
if subpattern_type == SubPatternType.RECTANGULAR:
for l in range(len(p)):
for r in range(l, len(p)):
here = sorted(p.perm[l:r + 1])
for x in range(len(here)):
want = set()
for y in range(x, len(here)):
want.add(here[y])
cur = []
for j in range(l, r + 1):
if p.perm[j] in want:
cur.append(p.perm[j])
if len(cur) <= max_len_patt:
valid.add(Permutation.to_standard(cur))
elif subpattern_type == SubPatternType.CONSECUTIVE:
for l in range(len(p)):
for r in range(l, len(p)):
if r - l + 1 > max_len_patt:
break
here = p.perm[l:r + 1]
if max(here) - min(here) + 1 == len(here):
valid.add(Permutation.to_standard(here))
if subpattern_type != SubPatternType.EVERY:
for i, p in enumerate(patterns):
sub[i] &= valid
def can_add(add, picked):
for p in picked:
if len(p) > len(add) and p.contains(add):
return False
if len(add) > len(p) and add.contains(p):
return False
return True
def bt(at, picked, seen):
if upper_bound is not None and len(picked) > upper_bound:
pass
elif picked == set(patterns):
pass
elif at == len(patterns):
yield picked
else:
for s in subsets(list(sub[at] - picked - seen)):
npicked = set(picked)
ok = True
for add in set(s):
if not can_add(add, npicked):
ok = False
break
npicked.add(add)
if not ok:
continue
found = False
for q in npicked:
if patterns[at].contains(q):
found = True
break
if not found:
continue
for res in bt(at + 1, npicked, seen | sub[at]):
yield res
for ps in bt(0, set(), set()):
# if remove_av_incr_decr:
# if any( p.is_increasing() for p in ps ) and any( p.is_decreasing() for p in ps ):
# continue
s = AvoiderPermutationSet(ps)
s._assure_length(settings.perm_bound)
here = set()
finite = False
for l in range(settings.perm_bound + 1):
found = False
for p in s.generate_of_length(l, {}):
here.add(Permutation(list(p)))
found = True
if not found:
finite = True
if finite and remove_finite and settings.sinput.is_classical:
break
if finite and remove_finite and settings.sinput.is_classical:
continue
here = {
Permutation(list(p))
for l in range(settings.perm_bound + 1)
for p in s.generate_of_length(l, {})
}
elems.append((s, here, None))
input = settings.sinput.get_permutation_set()
elems.append((InputPermutationSet(settings), input, None))
elems.append((N, set([Permutation([])]), None))
elems.append((P, set([Permutation([1])]), None))
res = DAG()
if remove:
for ps, here, descr in elems:
rem = set()
rems = []
for qs, other, odescr in elems:
if other and other < here:
rems.append(qs)
rem |= other
if not here - rem:
continue
if rems:
res.add_element(
SubtractPermutationSet(ps,
rems,
alone_ok=type(ps)
is not InputPermutationSet))
else:
res.add_element(ps)
else:
for ps, here, descr in elems:
res.add_element(ps)
for ps, here, descr in elems:
for qs, other, odescr in elems:
if other and other < here:
res.put_below(qs, ps)
ended = datetime.datetime.now()
settings.logger.log('Finished in %.3fs' %
(ended - started).total_seconds())
return res
# if avoids_231_vinc(p) and p.avoids([1,2,3]):
# cnt += 1
# # print(p)
# print(l, cnt)
#
# import sys
# sys.exit(0)
avoiders_len_3 = []
for p in Permutations(3):
avoiders_len_3.append((lambda perm: perm.avoids(p),StaticPermutationSet.from_predicate(lambda x: x.avoids(p), 6, description='Av(%s)' % str(p))))
# avoiders_len_3.append((lambda perm: len(perm) >= 3 and perm.avoids(p),StaticPermutationSet.from_predicate(lambda x: x.avoids(p), 6, description='Av(%s)' % str(p))))
incr = SimpleGeneratingRule(Permutation([1,2]), [X, P], description='increasing').to_static(8, empty)
decr = SimpleGeneratingRule(Permutation([2,1]), [X, P], description='decreasing').to_static(8, empty)
incr_nonempty = SimpleGeneratingRule(Permutation([1,2]), [X, P], description='increasing nonempty').to_static(8, {1:[Permutation([1])]})
decr_nonempty = SimpleGeneratingRule(Permutation([2,1]), [X, P], description='decreasing nonempty').to_static(8, {1:[Permutation([1])]})
max_len = 6
n_range = (2, 3) # number of rows (min, max)
m_range = (2, 3) # numbor of columns (min, max)
max_nonempty = 4
max_ec_cnt = 4
# permProp = lambda perm: perm.avoids([1,2])
permProp = lambda perm: perm.avoids([2,3,1])
# permProp = lambda perm: perm.avoids([1,4,2,3])
def incr(n):
return SimpleGeneratingRule(Permutation([1, 2]), [X, P],
description='increasing').to_static(n, empty)
def contains(self, perm):
if type(perm) is not Permutation:
perm = Permutation(list(perm))
return perm in self.permutations[len(perm)]
def generate_of_length(self, n, input):
rule = list(self.rule.items())
h = max(k[0] for k, v in rule) + 1 if rule else 1
w = max(k[1] for k, v in rule) + 1 if rule else 1
def permute(arr, perm):
res = [None] * len(arr)
for i in range(len(arr)):
res[i] = arr[perm[i] - 1]
return res
def count_assignments(at, left):
if at == len(rule):
if left == 0:
yield []
elif type(rule[at][1]) is PointPermutationSet:
# this doesn't need to be handled separately,
# it's just an optimization
if left > 0:
for ass in count_assignments(at + 1, left - 1):
yield [1] + ass
else:
for cur in range(left + 1):
for ass in count_assignments(at + 1, left - cur):
yield [cur] + ass
for count_ass in count_assignments(0, n):
for perm_ass in product(*[
s[1].generate_of_length(cnt, input)
for cnt, s in zip(count_ass, rule)
]):
arr = [[[] for j in range(w)] for i in range(h)]
for i, perm in enumerate(perm_ass):
arr[rule[i][0][0]][rule[i][0][1]] = perm
rowcnt = [
sum(len(arr[row][col]) for col in range(w))
for row in range(h)
]
colcnt = [
sum(len(arr[row][col]) for row in range(h))
for col in range(w)
]
for colpart in product(*[
ordered_set_partitions(
range(colcnt[col]),
[len(arr[row][col]) for row in range(h)])
for col in range(w)
]):
for rowpart in product(*[
ordered_set_partitions(
range(rowcnt[row]),
[len(arr[row][col]) for col in range(w)])
for row in range(h)
]):
ok = True
for idxs, perm_set in self.overlay:
res = [[None] * colcnt[col] for col in range(w)]
cumul = 1
for row in range(h - 1, -1, -1):
for col in range(w):
if (row, col) in idxs:
for idx, val in zip(
sorted(colpart[col][row]),
permute(
sorted(rowpart[row][col]),
arr[row][col])):
res[col][idx] = cumul + val
cumul += rowcnt[row]
perm = Permutation.to_standard(flatten(res))
if not perm_set.contains(perm):
ok = False
break
if not ok:
continue
res = [[None] * colcnt[col] for col in range(w)]
cumul = 1
for row in range(h - 1, -1, -1):
for col in range(w):
for idx, val in zip(
sorted(colpart[col][row]),
permute(sorted(rowpart[row][col]),
arr[row][col])):
res[col][idx] = cumul + val
cumul += rowcnt[row]
yield Permutation(flatten(res))
def rotate(perm):
n = len(perm)
return Permutation([ perm.index(n-i) + 1 for i in range(n)])
def generate_of_length(self, n, input):
if n == 1:
yield Permutation([1])
def test_taylor(self):
settings = StructSettings(perm_bound=7,
max_rule_size=(3, 3),
max_non_empty=3)
patts = [Permutation([1, 2, 3]), Permutation([3, 2, 1])]
settings.set_input(StructInput.from_avoidance(settings, patts))
res = taylor_dag(settings, remove=False)
self.check([], res)
patts = [Permutation([1, 2, 3]), Permutation([3, 2, 1])]
settings.set_input(StructInput.from_avoidance(settings, patts))
res = taylor_dag(settings, remove=False, remove_finite=False)
self.check([
[Permutation([1, 2]), Permutation([2, 1])],
[Permutation([1, 2, 3]),
Permutation([2, 1])],
[Permutation([1, 2]), Permutation([3, 2, 1])],
], res)
patts = [
Permutation([1, 2, 3]),
Permutation([3, 2, 1]),
]
settings.set_input(StructInput.from_avoidance(settings, patts))
res = taylor_dag(settings,
upper_bound=1,
remove=False,
remove_finite=False)
self.check([], res)
patts = [
Permutation([1, 2, 3]),
Permutation([3, 2, 1]),
]
settings.set_input(StructInput.from_avoidance(settings, patts))
res = taylor_dag(settings,
max_len_patt=2,
remove=False,
remove_finite=False)
self.check([
[Permutation([1, 2]), Permutation([2, 1])],
], res)
